Cohort Clustering With Log Normalization and Categorical Weighting
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Solution Overview
Problem
Existing clustering techniques struggle with managing large volumes of diverse data, particularly in IT environments, due to insufficient consideration of data semantics, overfitting, and inadequate normalization and handling of categorical data, leading to inefficient cohort identification.
Innovation Solution
The proposed solution includes selecting appropriate input dimensions using a logarithmic threshold, applying logarithmic normalization, and weighting categorical data indicators to ensure even Euclidean distance, followed by automated visualization and comparison of cohort structures across different dimensions, enabling effective cohort identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional clustering techniques are applied to large volumes of diverse data, then data processing can be performed, but the results suffer from insufficient consideration of data semantics, overfitting, and inadequate normalization leading to poor cohort identification accuracy
Solution Approach 1:
The patent applies logarithmic normalization to transform data parameters across different scales, converting raw values into logarithmic space to achieve uniform distribution. This parameter transformation resolves the overfitting issue by standardizing the scale of features while preserving semantic relationships, thereby improving cohort identification accuracy without proportionally increasing processing complexity
Solution Approach 2:
The patent performs preliminary data normalization and semantic feature extraction before applying clustering algorithms. By pre-processing the data to establish uniform scales and extract meaningful semantic features in advance, the system avoids the need for complex iterative adjustments during clustering, reducing overall processing complexity while enhancing identification accuracy
2Adaptability or versatility
If all generated data is stored for later analysis, then greater flexibility and comprehensive analysis are enabled, but storage costs and data management complexity increase significantly
Solution Approach 1:
The patent extracts only the essential semantic features and normalized representations from raw data before storage, rather than storing complete raw datasets. This extraction approach retains the critical information needed for flexible analysis while dramatically reducing storage requirements by eliminating redundant and non-essential data elements
Solution Approach 2:
The system performs preliminary feature extraction and normalization before data storage, preparing data in a compact, analysis-ready format. This pre-processing enables flexible cohort analysis to be performed on stored representations without requiring access to the original large-volume raw data, thus maintaining adaptability while reducing storage burden
3Reliability
If logarithmic normalization and dimension selection are applied, then signal integrity is maintained across varying scales, but additional processing steps increase computational overhead
Solution Approach 1:
The patent applies logarithmic transformation as a deterministic parameter change that can be computed efficiently using standard mathematical libraries. This transformation maintains signal integrity by preserving the relative relationships between values across different scales while being computationally less intensive than iterative normalization methods, thus minimizing additional processing time
Solution Approach 2:
The system applies logarithmic normalization selectively to only those dimensions identified as requiring scale adjustment, rather than uniformly processing all data. This partial application approach maintains signal integrity where needed while avoiding unnecessary computational overhead on already-normalized or less-critical dimensions, reducing overall processing time
4Measurement precision
If categorical data is properly weighted to ensure even Euclidean distance, then clustering accuracy improves, but the complexity of data preprocessing increases
Solution Approach 1:
The patent transforms categorical data into numerical representations with carefully assigned weights that ensure uniform contribution to Euclidean distance calculations. This parameter transformation converts complex categorical relationships into a standardized numerical format that integrates seamlessly with distance-based clustering algorithms, improving accuracy without requiring complex categorical handling logic
Solution Approach 2:
The weighting scheme for categorical data is designed to create equipotential conditions where each categorical feature contributes equally to the overall distance metric. This equipotential approach simplifies preprocessing by establishing uniform weighting rules that eliminate the need for complex, feature-specific normalization strategies, thereby improving clustering accuracy while maintaining manageable preprocessing complexity
Data Source
AI summary
This document discloses methods and systems for cohort identification. The methods and systems include improved calculations to perform cohort identification and practical applications of the improved calculations. Specifically, the systems and methods described herein may utilize key components that include enhancements of existing cohort clustering techniques with regard to selecting a number of cohort input dimensions, normalizing input data using a logarithm kernel-function, treatment of categorical data with mutually exclusive and not-mutually exclusive values, methods and visualization tool to determine appropriate number of cohorts, methods and visualization tool to compare cohorts extracted from different input dimensions, and methods to quantify the difference in cohorts. Beyond improvements to the cohort clustering techniques, also disclosed are ancillary tools to prepare input data by joining CRM and product usage data and facilitate subsequent automated action via an API to retrieve cohort results.